Phd Defence 25 Jan09

Supply Chain Performance Improvement: The Role of IT Presented By: Bibhushan Entry No: 2002RME027 Supervisors: Prof. S. Wadhwa and Prof. Anoop Chawla

Presentation Outline

Research Context and Motivation

Research Objectives

An Overview of the Research Work

Significant Contributions of Research Work

Publications

Response to Examiner’s Comments

Research Context and Motivation

Simulation for Supply Chain Modeling and Analysis

Used for analysis of complex systems

Type of problems modeled range from tactical to strategic

Object-Oriented Simulation Modeling

Detailed model of a complex system can be made by combining basic building blocks

Has advantages of inheritance, encapsulation, modularity, etc.

Multiple Entity Flow Perspective

Five flows: Material, Information, Money, Resource, Decision

Focus on Inventory Management to improve IT facilitated SC performance

Research Objectives

Highlight the research motivation to

Develop an object-oriented supply chain simulation-modeling environment

Develop demonstrative models to illustrate the efficacy of the approach in SC performance

Study the inventory management in supply chains working under stochastic demands

Research Objectives

Develop an object-oriented supply chain modeling and simulation environment based on multiple-entity flow perspective which should be capable of:

Modeling the flow of multiple entities

Stochastic modeling

Adding user-defined decision rules in addition to major control decision rules

User-friendly and cost effective

Robust modeling by means of effective error handling and fool-proofing in data input

Distributed simulation

Research Objectives

Analyze inventory management along multiple criteria (demand variance, inventory, service level etc.)

Understand the effect of Expected Service Quality (ESQ) on different inventory policies

Determine optimal ESQ for each node

Determine optimal Information sharing level for the ESQ levels found above

Understand the effect of ordering and capacity constraints on different inventory policies

Determine Optimal Ordering and Capacity constraints for each node

Determine the optimal information sharing level for ordering and capacity constraint levels determined above

Determine the effect of change in Coefficient of Variance (COV) on each supply chain node

Determine the optimal Information sharing level for different COV levels

Overview of Research Work

Organization of Thesis

Conceptual Framework

Simulation Modeling Environment

Performance of Supply Chain under Controlled Variability

Optimizing ESQ for Supply Chain Nodes

Optimizing Optimal Ordering and Capacity Constraint levels for Each Supply Chain Node

Understand the Effect of Changing COV on supply chain

Conceptual Framework

A Generic Model of Supply Chain

Object Oriented Modeling Perspective

Modeling of Elementary Supply Chain Constructs

Hierarchy of Object Used in Supply Chain Modeling

Modeling Supply Chain Decisions

Modeling the Supply Chain Building Blocks

Modeling the manufacturing system

Modeling the transports

Modeling the Player Role

Modeling the Supply Chain Node

Modeling the Inter-Node Interactions

Defining Inter-Node Relationships

Defining Inter-Node Lead Times

Defining Inter-Node Speeds

Defining Inter-Node Distances

Defining Product Demands

Source Selection Policies

Inventory Control Decisions

Transportation Decisions

Production Planning Decisions

Performance Metrics

Inventory Related

Demand Related

Service Related

Supply Chain Model for Research

Model Verification and Validation

Experimental Setup

Demand impulses

Simulation parameters

Balancing the inventory policies

Performance metrics considered

Effect of Transformed Relative Impulse Amplitude (TRIA) on the Supply Chain

Effect of TRIA on the Supply Chain using Demand Flow Policy (DFP)

Effect of TRIA on the Supply Chain using Order Q Policy (OQP)

Effect of TRIA on the Supply Chain using (s, S) Policy (sSP)

Effect of TRIA on the Supply Chain using (s, Q) Policy (sQP)

Effect of Balance Gap (BG) on the Supply Chain

Effect of BG on the Supply Chain using DFP

Effect of Negative Impulse BG (NIBG)

Effect of Positive Impulse BG (PIBG)

Effect of BG on the Supply Chain using OQP

Effect of NIBG

Effect of PIBG

Effect of BG on the Supply Chain using sSP

Effect of NIBG

Effect of PIBG

Effect of BG on the Supply Chain using sQP

Effect of NIBG

Effect of PIBG

Effect of Number of Impulses (NI) on the Supply Chain

Effect of NI on the Supply Chain using DFP

Effect of Number of Negative Impulses (NNI)

Effect of Number of Positive Impulses (NPI)

Effect of NNI

Effect of NPI

Effect of NNI

Effect of NPI

Effect of NNI

Effect of NPI

Effect of Impulse Width (IW) on the Supply Chain

Effect of IW on the Supply Chain using DFP

Effect of Negative Impulse Width (NIW)

Effect of Positive Impulse Width (PIW)

Effect of NIW

Effect of PIW

Effect of NIW

Effect of PIW

Effect of NIW

Effect of PIW

Supply Chain Processes

Plan

Balances aggregate demand and supply

Source

Procures goods and services to meet planned or actual demand

Make

Transforms product to a finished state

Deliver

Provides finished goods and services

Return

Post-delivery customer support

A Generic Model of Supply Chain

Supply Chain Flows

Primary Flows (Between Nodes)

Material Flow

Information Flow

Cash Flow

Secondary Flows (Only inside Node)

Resource Flow

Decision Flow

Object Oriented Supply Chain Simulation

Simulation is a technique where computers imitate the operations of various kinds of real-world facilities or processes (Law and Kelton 1991)

Discrete-event simulation

Object oriented modelling

OOPs based simulator for modeling flexible supply chains

Need for Object Oriented Supply Chain Simulation

Supply chain flexibility offers many challenges and opportunities

It offers decision choices as the system evolves which is dynamic in nature

There is a need for developing a modeling environment to deal with flexibility and dynamic decision making

A OOPs based simulation system is developed and explored for its efficacy in this research

Advantages of Object Oriented Modeling

Inheritance

A class of objects can itself be linked to one or several super-classes from which it acquires characteristics and behavior

Encapsulation

Describes its characteristics along with its relationships to other components and the functionality of the object

Allows structured development of the model

Hides unimportant details

Modularity

Provides a very high degree of code reusability

Allows the model builder to develop the models with much less effort

Suitable for modeling distributed systems having client-server architecture

Plug-and-play software capability

Interoperability across the network

Platform independence

Allows complex systems to be constructed with minimum of redundant work

A logical choice for developing custom or dedicated simulation models

Sub-components may be prefabricated by some expert group for a specific need or application

Productivity of software development improves if code is reused, since the specific modules are already extensively tested by their developers

Provides a natural mapping paradigm which allows one-to-one mapping between objects in the system being modeled and their abstractions in the object model

Allows the developer to achieve a faster transition of the conceptual model into the software implementation

Object-oriented models generally have a cleaner structure than the event oriented ones

Overall Architecture

Basic building blocks are used to create some lower level complex objects

Lower level objects are then used to define the higher level objects

Level 1 objects are made up of basic building blocks

Basic building blocks are combined with the object(s) of level 1 to form level 2 objects

Object Oriented Modeling of Supply Chains

Supply chain decision making requires rapid and flexible modeling approach at various levels of detail

Object oriented modeling can be used for

Designing and implementing reusable classes for building models of supply chains

Creating a supply chain object library

Facilitates rapid model development

Aid in application of the modeling architecture to specific scenarios at various levels of abstraction

Object Oriented Features in Arena Simulation Environment

Offers model development in object oriented manner by means of objects called “modules”

Modules are essentially composed of other basic level modules

Once properly developed, these modules can be reused in other simulation models

However…

Limitations of Object Oriented Features in Arena

Modules can be run only on systems having ARENA

Version Conflicts

Not suitable for distributed computing

Cost of buying this simulation package

Additional cost of buying the customized module libraries

What is the solution then?

Generic Programming Languages

Not as easy as developing models using simulation packages

However,

It is more general and the SC flexibility related issues can be modeled in detail.

Availability of customized object libraries for a variety of applications can significantly reduce the time and effort involved in model building process

It offers platform independence to a large extent

IT tool used: VB.Net

Ease of designing the user interface

Now fully object-oriented

Provides a very high degree of platform independence

only for Windows based platforms however

Supply chain flexibility and dynamic decision making can be developed as a customized option.

Research Gaps

Need to develop simulation tools ideally suited for flexible supply chain simulation

Effective modeling of Supply Chain Flexibility

Web-based simulation environment

Demonstrate benefits of collaborative decision making

Non-deterministic and dynamic modeling

Analyzing the impact of different control decisions in an integrated manner

Distributed computing needs to be explored

Research Gaps

Need to study the impact of information sharing under different IT options

Supply Chain performance under different levels of Demand History, Service Level, Demand Variance needs to be studied

There is need for demonstrative models to illustrate the benefits of IT tools focused on modeling of the flexible supply chains.

Overview of the Research Work

Development of the IT tools for modeling Flexibility and Dynamic decision making

Manufacturing systems and supply chains were modelled in terms of five types of flows: information flow, decision flow, material flow, resource flow and money flow

Extended the Multiple Entity flow perspective proposed by Wadhwa & Rao (2003)

Development of demonstrative simulation models for illustrating supply chain performance improvement by the use of IT

Overview of the Research Work

Supply Chain performance improvement under flexibility and dynamic decision making. Focus on inventory management.

Comparison of Inventory Control Policies under Deterministic Variability

Effect of Demand History on Supply Chain Performance

Effect of Service Level on Supply Chain Performance

Effect of Demand Variance on Supply Chain Performance

Supply Chain Management Defined

SCM is “the integration of business processes from end-user through original suppliers that provides products, services, and information that add value for customers” (Lambert et. al. (1998)

Modeling Elementary Supply Chain Constructs

Classification of Objects

Action Points as Processes in the System

Classification of Objects

Action Points as Processes in the System

Hierarchy of Object Used in Supply Chain Modeling

Modeling of a Supply Chain Network

Modeling of Supply Chain Nodes

Modeling of Supply Chain Operations

Modeling the Manufacturing System

Levels of Abstraction for Supply Chain Modeling

Modeling of a Supply Chain Network

As a collection of supply chain nodes

Each node is a fully autonomous unit

Define relationships between each pair of nodes

Two types of relationships

Buyers (can select Sellers)

Sellers (can only be selected)

Constrained relationships

By the level of respective nodes

Integration of Supply Chain Nodes

Multiple Supply Chains in a Collection of Supply Chain Nodes

Two kinds of Nodes:

Manufacturing (Value-adding)

Non-Manufacturing ( store the material and supply it to other nodes )

Flows through each node:

Material flow

Information flow

Money flow

Flows Inside node

Resource Flow

Decision Flow

Five Processes

Plan, Source, Make, Deliver and Return

Return

Out of scope of this work

Store

Additional Process

A Manufacturing Node

A Non-manufacturing Node

Integration of Major Supply Chain Operations

Make

Manufacturing operations

Product quantity is decided by planning

Produces the goods according to the control policies determined by production planning

Routing

Scheduling

Source

Decides the sellers from whom to procure necessary goods

A sourcing policy is a decision rule that determines the best seller(s) out of a number of available sellers in accordance with some predefined criterion e.g. Maximum Inventory, Minimum Lead Time etc.

Deliver (Transportation)

Out of a number of transports one or more transports are selected based on some pre-defined transportation policy like

Maximum Speed

Minimum Cost

Maximum Capacity

Inventory Management

Concerned with maintenance of sufficient amount of inventory to fulfil demands

Whenever the inventory of any item falls below the critical levels, the inventory management sends the order(s) to procure the required material or product to planning operation

Planning subsequently decides either to make or buy the required product

Can be modeled by combining two basic building blocks

Materials (Transformed or Consumed)

Resources (Negligible Transformation/Consumption)

One or more resource is used to perform some operation on the material (called process )

Each product requires some processes to be performed in a specific sequence

Two decisions are taken before each process

Resource selection

Material selection

Typical Material Processing in a Manufacturing System

Classification (based on no. of sources)

Single Source

Multiple Source

Transport Based.

Source Selection Rules

Shortest distance

Minimum cost

Maximum inventory

Preference selection

Probability based selection (only for multiple source policies)

User defined selection (only for single source policies)

Probability Based Source Selection

Multiple Source Selection

Demand Flow

Order Q

Order Upto

(s, Q) Policy

(s, S) Policy

Updated (s, S) Policy

Days of Supply, Demand Based (DOS Demand)

Days of Supply, Forecast Based ( DOS Forecast)

Updated (s, S) Policy

Reorder Level ( s ) is calculated as

Where

LT is the lead time of the selected source

σ is the estimate of standard deviation of the demand in previous n periods

Z is the standard normal variate corresponding to the desired service level

Special Cases

n -period moving average ( Z = 0 )

Demand Flow ( n = 1, Z = 0 )

DOS Demand

DOS Forecast

Each transport uses some transportation mode e.g. rail , road , air , or water

Depending on the location, not all transports may be possible for a node pair

Alternative transports are selected based on which transport modes are available between a node pair

Alternative transports differ according to their specific characteristics

Each transport has some properties like capacity speed, cost, etc

Transport selection rules

Maximum speed

Maximum volume capacity

Maximum weight capacity

Minimum cost

User defined transport selection

Transportation Selection Policies

More Classifications in Transports

The loading in the selected transport may again be of two types

Pooled (all the products shipped between a node-pair are sent through the same transport)

Non-pooled (different products are shipped through different transports)

Based on capacity utilization of transport

FTL (Full Truck Load)

LTL (Less than Truck Load)

Transport selection with pooled transports

Transport selection with non pooled transports

Routing

Concerned with selection of best possible resources out of a number of available resources

Scheduling

Decides the timing of each process or each job in the manufacturing system

Quantity to be produced is determined by the inventory policy

Options Available

Produce as directed by inventory policy

Produce short batches

Once the product is routed to a resource, it is added to the queue of the corresponding resource

Routing policy is used to select the next resource for the next process when current processing is over

Sequence of resource selection and allocation continues until all the processes on the job are completed

Production Planning Operation

Determining the Make Quantity

Resource Selection Policies

Modeling the Supply Chain Building Blocks

Consists of

Modeling the Transports

Modeling Node Interactions

Different objects in the SC Network are linked with each other, they can be represented using the concepts of Relational Database Management System (RDBMS)

Modeling the Transports

Performance Metrics

Inventory related

Minimum Inventory

Maximum Inventory

Total Inventory

Average Inventory

Standard Deviation of Inventory

Performance Metrics Used

Service related

Backorders

Stockouts

Fill Rate

Service Level

Demand related

Minimum Demand

Maximum Demand

Total Demand

Average Demand

Standard Deviation of Demand

Supply Chain Performance Under Deterministic Variability

Experimental Setup

Demand Impulses

Simulation Parameters

Balancing the Inventory Policies

Effect of Impulse Amplitude

Effect of Impulse Width

Effect of Step Width

Effect of Number of Impulses

Demand Impulses

Simulation Parameters Variable 1 1 1 Impulse Width 1 Variable 1 1 Number of Impulses 0 0 Variable 0 Balance Gap 0.9 and 1.9 0.9 and 1.9 0.9 and 1.9 Variable Impulse Amplitude 100 100 100 100 Mean Demand 2 2 2 2 Transportation Lead Time (Days) 2 2 2 2 Information Lead Time (Days) 90 90 90 90 Observation Period (Days) 20 20 20 20 Warmup Period (Days) 110 110 110 110 Run Length (Days) Step Width Number of Impulses Balance Gap Demand Amplitude Parameter

Balancing the Policies

Each policy was balanced so that all of them gave same results for the test demand under steady state condition

Demand Flow: The test demand was a constant demand of 100 units per week. To fulfill the current obligations, each node has to keep a minimum of 100 units. Each node has to keep an initial inventory equal to four weeks of demand. As a result, an initial inventory of 400 units was allocated to each node.

Order Q: In this policy, orders are placed even when no there is no demand. Therefore, inventory builds up for each node, until the actual demand is received. As a result, all nodes only need to keep an inventory equal to the value of demand per week (100 units).

Balancing the Policies

(s, Q) Policy: The initial inventories for each node were same as those for demand flow policy. A reorder point ( s ) of 400 and order quantity ( Q ) of 100 was set for this policy.

(s, S) Policy: Initial inventories were kept same as the demand flow policy. Both reorder point ( s ) and reorder level ( S ) were set to be 100 units.

Effect of Impulse Amplitude

Effect on Individual Supply Chain Nodes

Effect on Retailer

Effect on Wholesaler

Effect on Distributor

Effect on Manufacturer

Effect of each policy on the Supply Chain

Effect along the Supply Chain

Effect of Amplitude on Individual Supply Chain Nodes

Total Inventory

Std. Dev. of Inventory

Backorders

Stockouts

Std. Dev. of Demands

Retailer’s Total Inventory

Retailer’s Std. Dev. of Inventory

Retailer’s Backorders

Retailer’s Stockouts

Wholesaler’s Total Inventory

Wholesaler’s Std. Dev. of Inventory

Wholesaler’s Backorders

Wholesaler’s Stockouts

Wholesaler’s Std. Dev. of Demand

Distributor’s Total Inventory

Distributor’s Std. Dev. of Inventory

Distributor’s Backorders

Distributor’s Stockouts

Distributor’s Std. Dev. of Demand

Manufacturer’s Total Inventory

Manufacturer’s Std. Dev. of Inventory

Manufacturer’s Backorders

Manufacturer’s Stockouts

Manufacturer’s Std. Dev. of Demand

Effect of Amplitude on Supply Chain as a Whole Demand Flow Policy Order Q Policy (s, S) Policy (s, Q) Policy

Demand Flow Policy

Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

Order Q Policy

Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

(s, S) Policy

Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

(s, Q) Policy

Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

Effect of Amplitude on the Supply Chain as a Whole

Type of Impulse

Positive Impulse (0.9)

Negative Impulse (-0.9)

Total Inventory

Backorders

Stockouts

Std. Dev. of Demands

Negative Impulse Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

Positive Impulse Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

Effect of Demand History on Supply Chain Performance

Experimental Setup

No Information Sharing

Effect on Individual Nodes

Effect on Whole Supply Chain and Effect along the Supply Chain

Partial Information Sharing

Full Information Sharing

Comparison of Information Sharing Levels

No Information Sharing

Effect on Individual Supply Chain Nodes Retailer Wholesaler Distributor Manufacturer

Retailer’s Total Inventory

Retailer’s Maximum Inventory

Retailer’s Std. Dev. of Inventory

Retailer’s Backorders

Retailer’s Stockouts

Wholesaler’s Total Inventory

Wholesaler’s Std. Dev. of Inventory

Wholesaler’s Backorders

Wholesaler’s Stockouts

Wholesaler’s Std. Dev. of Demand

Distributor’s Total Inventory

Distributor’s Std. Dev. of Inventory

Distributor’s Backorders

Distributor’s Stockouts

Distributor’s Std. Dev. of Demand

Manufacturer’s Total Inventory

Manufacturer’s Std. Dev. of Inventory

Manufacturer’s Backorders

Manufacturer’s Stockouts

Manufacturer’s Std. Dev. of Demand

Effect on Supply Chain and Effect Along the Supply Chain

Total Inventory

Backorders

Stockouts

Std. Dev. of Demand

Significant Contributions of the Research Work

Development of an object-oriented supply chain simulation environment

Role of IT based tools are developed and used to study IT facilitated information and decision flows in flexible supply chains is studied

Development of a framework that incorporates different IT facilitated control policies in SCs

Comparison for inventory policies under deterministic variability and information sharing

Analysis of Supply chain performance under different levels of demand information (IT focus)

Significant Contributions of the Research Work

Analysis of Supply chain performance under different of Service levels

Analysis of Supply chain performance under different levels of demand variance

Analysis of supply chain performance under different level of information sharing with

Different levels of demand history

Different service levels

Different demand variances

Limitations and Scope for Future Research

The modeling environment can be extended in more directions like

Closed loop supply chains by adding return process

Manufacturing operations can be extended to include different kinds of production facilities

….

Focus on Inventory management only…

List of Publications

Published/Accepted for Publication

Postponement strategies for re-engineering of automotive manufacturing: knowledge-management implications , International Journal of Advanced Manufacturing Technology, Article in Press, doi 10.1007/s00170-006-0679-z.

Hybrid Tabu-Sample Sort Simulated Annealing (SSA) with Fuzzy Logic Controller: CIM System Context , Studies in Informatics and Control, June 2006, Volume 15, Number 2.

Flexible Supply Chains: A Context for Decision Knowledge Sharing and Decision Delays , Global Journal of Flexible Systems Management, Volume 7 Numbers 3 & 4, July -Dec 2006 (Accepted for Publication).

Impact of Supply Chain Collaboration on Customer Service Level and Working Capital , Global Journal of Flexible Systems Management (Accepted for Publication).

List of Publications

Under Review

A multi-criteria customer allocation problem in supply chain environment: an artificial immune system with fuzzy logic controller based approach , International Journal of Computers Communication and Control.

Inventory performance of some supply chain inventory policies under impulse demands , International Journal of Production Research, Manuscript ID: TPRS-2007-IJPR-0111.

Communicated

An Object Oriented Framework for Modeling Control Policies in a Supply Chain , International Journal of Value Chain Management

List of Publications National / International Conferences

Web Based Virtual Supply Chain Modeling to Enhance Learning , The International Conference on e-Learning (ICEL 2006), University of Quebec in Montreal, Canada, June 22-23.

Supply Chain Modeling: The agent based Approach , 12th IFAC Symposium on Information Control Problems in Manufacturing (INCOM-2006), Saint-Etienne, France, May 17-19.

Object-Oriented Approach for Simulation of Supply Chain , International Congress on Logistics and SCM Systems (ICLS-2006), Kaohsiung, Taiwan, May 1-2.

Comparison of some Supply Chain Management Software Applications , National Conference on Advances in Mechanical Engineering (AIME-2006), January 20-21.

Response to Examiner’s Comments

Comments of Examiner 1

No Information Sharing: Is it best for individual wholesalers and retailers?

As we move higher in the supply chain, the demand variability increases because of the inventory policy used

It is not that no information sharing is good for individual wholesalers and retailers, information sharing is just less important for them.

Full Information Sharing: Is it best for the overall system?

Whether full information sharing is best for the system or not is dependent on the inventory policy used

The thesis aims to demonstrate that after some particular level of information sharing, the investment in IT may not be economically justified

If answers to (1) and (2) above is yes, then explain why optimization on IS level (information sharing) is necessary? Why would an intermediate value (of IS) would be optimal? Whose objective have you considered? Individual wholesalers/retailers or the whole system? Or a combination of the two?

It is important to find the level and type of information sharing

On page xxvii: IT should be information technology

The required change has been made.

Uncertainty in supply chain is demand side and the lead time size. When you consider disturbances: you could have considered lead time disturbances.

We consider this as a future area of research

Advanced IT means: continuous review policies (for inventory). What implications does it have for your thesis?

In a continuous review policy, the inventory position is continuously monitored

Review period is one day; all the policies in our research are the continuous review policies

For single node (such as wholesaler or retailer): given demand and lead time uncertainty: optimal policy for lot sizing can be devised. Then it could be used in your simulation.

Decentralized decision making is found to deteriorate the supply chain performance

The decisions of one node may indirectly affect the performance of other (interaction effects)

A schematic diagram of supply chain (number of plants), distributor (numbers) and the wholesaler/retailers (numbers) considered in the thesis can be given.

A schematic diagram of the supply chain considered in this thesis is given on page 136. Description of the same is given in section 4.5

Main focus of thesis is determination of optimal levels of controllable factors such as … modifying the thesis title.

Motivation of this research is to bring the information technology (IT) as a performance improvement solution in the supply chain

Information sharing and IT are mutually complimentary

The research highlights where and how much information needs to be shared for the optimal performance

Factors beyond the control of decision makers (uncertainty) and factors under decision-makers’ control … readability of the thesis.

The required tables have been added in the Appendix A

The current developed framework is limited to only two players, i.e. manufacturing and inventory … more than three players?

There are four players in the supply chain considered in this research: Retailer , Wholesaler , Distributor and Manufacturer

In addition to the inbuilt player roles like supplier, manufacturer, distributor, wholesaler and retailer, users can also define their own Player Roles .

Network manufacturing is a new arena for modern manufacturing environment. How could … contribution in this field?

For network manufacturing also, this framework can still handle the execution side

In network manufacturing, the manufacturing of the finished product takes place through a coordination of multiple autonomous players. Such a network will have most of the players as manufacturing type players.

This framework can be used where higher level modeling of the manufacturing system is sufficient

In this research, “overall supply chain cost” has been used as the major criterion for the supply chain performance. In fact, there are many Key Performance Indicators (KPI) reported in the supply chain management research work, such as agilability, lead time, flexibility, expandability, trust, etc. How could you consider these issues into your research framework?

The research framework, in its present form, has only the KPIs which were required for this research work, i.e. those related to inventory management

Since the framework is based on object oriented methodology, multiple KPI libraries can be added to it as and when need arises

What are the major bottlenecks in the implementation of the developed framework in real-life industrial case?

The framework has been developed considering a very generic nature of the supply chain

What are the major limitations of the developed framework in this thesis?

The return operation of a supply chain is not available in the framework.

In the future, some other major supply chain operations may be added in the framework.

The effectiveness of the simulation environment can be immensely improved by incorporating some optimization algorithms for simpler supply chain decisions and some meta-heuristics for complex problems.